PPT-Queueing Theory Operations -- Prof. Juran

2 Overview Basic definitions and metrics Examples of some theoretical models Operations Prof Juran 3 Basic Queueing Theory A set of mathematical tools for the analysis of probabilistic systems of customers and servers . At each of the stations a service is provided that takes up time Workpieces approach the stations and are processed immediately if the server is idle Otherwise the workpieces line up in the buffer in front of a station and wait to receive service Ap We now turn our attention to closed networks Closed Queueing Models Closed networks are extremely important for computer systems modeling because they capture the notion of interactivity in the system In an open network customers simply arrive recei Operations -- Prof. Juran. 2. Overview. Basic definitions and metrics. Examples of some theoretical models. Operations -- Prof. Juran. 3. Basic Queueing Theory. A set of mathematical tools for the analysis of probabilistic systems of customers and servers. . Decision Models -- Prof. Juran. 2. Overview. Finance Simulation Models. Forecasting. Retirement Planning. Butterfly Strategy. Risk Management. Introduction to . VaR. Currency Risk. Using . Historical Data in Simulations. Operations Management -- Prof. Juran. 2. Outline. Optimization Extensions . Multiperiod Models. Operations Planning: Sailboats. Ne. twork Flow Models . Transportation Model: Beer Distribution. Assignment Model: Contract Bidding. GraphG prof 1 prof 2 prof 3 prof 4 phd 5 stud 6 stud 7 adv adv adv adv adv sup sup GraphI 1 prof 2,3 prof 4 prof 5 phd 6,7 stud adv adv adv sup 1Fortheformaldevelopmentinthispaper,itwillbeconve-nientt GraphG prof 1 prof 2 prof 3 prof 4 phd 5 stud 6 stud 7 adv adv adv adv adv sup sup GraphI 1 prof 2,3 prof 4 prof 5 phd 6,7 stud adv adv adv sup 1Fortheformaldevelopmentinthispaper,itwillbeconve-nientt Theory. Lindsay Mullen. Seminar Presentation #2. November 4, 2013. Computer Science Applied Mathematics. . Discrete Mathematics. Operation Research. Queueing. Theory. Operation . Research. Decision Models -- Prof. Juran. 2. Example: Preventive Maintenance. At the beginning of each week, a machine is in one of four conditions: 1 = excellent; 2 = good; 3 = average; 4 = bad. . The weekly revenue earned by a machine in state 1, 2, 3, or 4 is $100, $90, $50, or $10, respectively. . Decision Models -- Prof. Juran. 2. Overview. Forecasting Methods . Exponential Smoothing. Simple. Trend (Holt’s Method). Seasonality (Winters’ Method). Regression. Trend. Seasonality. Lagged Variables. Decision Models -- Prof. Juran. 2. Overview. Evolutionary Solver. (Genetic Algorithm). Advertising Example. Product Design Example. Conjoint Analysis. Decision Models -- Prof. Juran. 3. Nonlinear Problems. 2. Overview. Sensitivity Analysis. Goal Seek and Data Table. Marketing and Finance examples. Call Center LP. More Sensitivity Analysis. SolverTable. Decision Models -- Prof. Juran. 3. Sensitivity Analysis. 2. Overview. Monte Carlo Simulation. Basic concepts and history. Excel Tricks. RAND(), IF, Boolean. Crystal Ball. Probability Distributions . Normal, Gamma, Uniform, Triangular. Assumption and Forecast cells. Session 10b Decision Models -- Prof. Juran 2 Overview Marketing Simulation Models New Product Development Decision Uncertainty about competitor behavior Uncertainty about customer behavior Market Shares